Data Scientist & AI Practitioner | Machine Learning | NLP | End-to-End Model Deployment

I am an AI & Data Science Practitioner dedicated to building practical, scalable, and data-driven solutions. With a strong foundation in Machine Learning, Natural Language Processing (NLP), and Predictive Analytics, I specialize in transforming raw data into actionable business intelligence. My expertise spans the entire development lifecycle, from data cleaning and feature engineering to deploying models using Flask and FastAPI.

Machine Learning: Proficient in building classification and regression models using Scikit-learn, XGBoost, and TensorFlow.

NLP & Text Analytics: Experienced in developing sentiment analysis engines and recommendation systems using TF-IDF, tokenization, and similarity scoring.

Data Engineering: Skilled in complex data manipulation, exploratory data analysis (EDA), and handling class imbalances with techniques like SMOTE.

Deployment & Tools: Capable of containerizing applications with Docker and utilizing AWS for basic cloud deployments.

Customer Retention: Developed an end-to-end pipeline for Customer Churn Prediction, identifying high-risk users to support proactive business strategies.

Public Sentiment: Built a Twitter Sentiment Analysis engine with 77% accuracy to interpret real-time public opinion.

Leadership & Strategy: Led a team during the IBM Z Datathon to design a predictive system for court case prioritization, focusing on judicial efficiency.

I am currently pursuing a Bachelor of Technology in Artificial Intelligence and Data Science (CGPA: 7.2). To stay at the forefront of the industry, I have earned certifications in Prompt Engineering, Data Visualization (Infosys), and building real-world AI applications with Google Cloud’s Gemini & Imagen.